LongHorizon-Harness is a computer-use harness that lets AI agents continue work across desktop applications and the command line for extended periods by planning, acting, verifying, checkpointing, and recovering. It is for users who need Claude Code, Codex, OpenCode, or DeepSeek Harness to make reliable progress on complex long-running workflows without training a new model. The catalogue entries provide skills for operating this execution loop.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-taskgit clone --depth 1 https://github.com/AMAP-ML/LongHorizon-HarnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amap-ml/longhorizon-harness/analyze-task)<a href="https://agentmods.dev/skills/amap-ml/longhorizon-harness/analyze-task"><img src="https://agentmods.dev/badge/skills/amap-ml/longhorizon-harness/analyze-task/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/amap-ml/longhorizon-harness/analyze-task"><img src="https://agentmods.dev/badge/skills/amap-ml/longhorizon-harness/analyze-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00186 |
| Opus 5 | $0.00022 | $0.00093 |
| Sonnet 5 | $0.00009 | $0.00037 |
| Haiku 4.5 | $0.00004 | $0.00019 |
Grade A, and why
analyze-task scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
If only one task is issued, analyze it directly with instruction: analyze-single-task.md.
If multiple tasks are issued, use subagents to analyze them in parallel (one agent for each task) and save reports under check/<task_id>/. Do not analyze them sequentially by yourself. DO NOT tell it what to do. Just ask the subagent to analyze the task in target directory and use this skill (analyze-task) to do the analysis. Pass any user instructions to every subagent.
After the subagents finish, do nothing but report to user that the check is done and where to find the reports.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 11 lines · 44 tokens per session scan A 7036bd90775d
analyze-task is a skill published in the GitHub repository AMAP-ML/LongHorizon-Harness (1,481 stars, last pushed 20d ago), licensed MIT. It adds 44 tokens to every session and 186 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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